Advertisement

Reverse Engineering Gene Regulatory Networks Related to Quorum Sensing in the Plant Pathogen Pectobacterium atrosepticum

  • Kuang Lin
  • Dirk Husmeier
  • Frank Dondelinger
  • Claus D. Mayer
  • Hui Liu
  • Leighton Prichard
  • George P. C. Salmond
  • Ian K. Toth
  • Paul R. J. Birch
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 673)

Abstract

The objective of the project reported in the present chapter was the reverse engineering of gene regulatory networks related to quorum sensing in the plant pathogen Pectobacterium atrosepticum from micorarray gene expression profiles, obtained from the wild-type and eight knockout strains. To this end, we have applied various recent methods from multivariate statistics and machine learning: graphical Gaussian models, sparse Bayesian regression, LASSO (least absolute shrinkage and selection operator), Bayesian networks, and nested effects models. We have investigated the degree of similarity between the predictions obtained with the different approaches, and we have assessed the consistency of the reconstructed networks in terms of global topological network properties, based on the node degree distribution. The chapter concludes with a biological evaluation of the predicted network structures.

Key words

Pectobacterium atrosepticum Quorum sensing Transposon mutagenesis Microarrays Graphical Gaussian models Sparse Bayesian regression LASSO Bayesian networks Nested effects models Degree distribution Power law Gene ontologies 

References

  1. 1.
    Liu, H., Coulthurst, S. J., Pritchard, L., Hedley, P. E., Ravensdale, M., Humphris, S., Burr, T., Takle, G., Brurberg, M.-B., Birch, P. R. J., Salmond, G. P. C. and Toth, I. K. (2008) Quorum sensing coordinates brute force and stealth modes of infection in the plant pathogen Pectobacterium atrosepticum. PLoS Pathogens, 4, 29.Google Scholar
  2. 2.
    Yang, Y., Dudoit, S., Luu, P. and Speed, T. (2001) Normalization for cDNA microarray data. In Bittner, M., Chen, Y., Dorsel, A. and Dougherty, E. (eds.), Microarrays: Optical Technologies and Informatics, volume 4266 of Proceedings of SPIE.Google Scholar
  3. 3.
    Smyth, G. K. (2004) Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology, 3, Article 3.Google Scholar
  4. 4.
    Smyth, G. K. (2005) Limma: linear models for microarray data. In Gentleman, R., Carey, V., Huber, W., Irizarry, R. and Dudoit, S. (eds.), Bioinformatics and Computational Biology Solutions Using R and Bioconductor, Statistics for Biology and Health, pp. 397–420. Springer, New York.CrossRefGoogle Scholar
  5. 5.
    Lönnstedt, I. and Speed, T. (2002) Replicated microarray data. Statistica Sinica, 12, 31–46.Google Scholar
  6. 6.
    Hastie, T., Tibshirani, R. and Friedman, J. (2001) The Elements of Statistical Learning. Springer-Verlag, New York.Google Scholar
  7. 7.
    Beal, M. J. (2003) Variational algorithms for approximate Bayesian inference. Ph.D. thesis, Gatsby Computational Neuroscience Unit, University College London.Google Scholar
  8. 8.
    Butte, A. S. and Kohane, I. S. (2000) Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pacific Symposium on Biocomputing, 2000, 418–429.Google Scholar
  9. 9.
    Werhli, A. V., Grzegorczyk, M. and Husmeier, D. (2006) Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical Gaussian models and Bayesian networks. Bioinformatics, 22, 2523–2531.PubMedCrossRefGoogle Scholar
  10. 10.
    Edwards, D. M. (2000) Introduction to Graphical Modelling. Springer Verlag, New York.CrossRefGoogle Scholar
  11. 11.
    Schäfer, J. and Strimmer, K. (2005) A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Statistical Applications in Genetics and Molecular Biology, 4, Article 32.Google Scholar
  12. 12.
    Opgen-Rhein, R. and Strimmer, K. (2007) From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Systems Biology, 1, 37.PubMedCrossRefGoogle Scholar
  13. 13.
    Williams, P. M. (1995) Bayesian regularization and pruning using a Laplace prior. Neural Computation, 7, 117–143.CrossRefGoogle Scholar
  14. 14.
    Tibshirani, R. (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B, 58, 267–288.Google Scholar
  15. 15.
    van Someren, E. P., Vaes, B. L. T., Steegenga, W. T., Sijbers, A. M., Dechering, K. J. and Reinders, M. J. T. (2006) Least absolute regression network analysis of the murine osterblast differentiation network. Bioinformatics, 22, 477–484.PubMedCrossRefGoogle Scholar
  16. 16.
    Grandvalet, Y. and Canu, S. (1998) Outcomes of the equivalence of adaptive ridge with least absolute shrinkage. In Kearns, M., Solla, S.A. and Cohn, D.A. (eds.), Advances in Neural Information Processing Systems 11, pp. 445–451. MIT Press, CambridgeGoogle Scholar
  17. 17.
    MacKay, D. J. C. (1996) Hyperparameters: optimize, or integrate out. In Heidbreder, G. (ed.), Maximum Entropy and Bayesian Methods, pp. 43–59. Kluwer Academic Publisher, Santa Barbara.CrossRefGoogle Scholar
  18. 18.
    MacKay, D. J. C. (1992) Bayesian interpolation. Neural Computation, 4, 415–447.CrossRefGoogle Scholar
  19. 19.
    Rogers, S. and Girolami, M. (2005) A Bayesian regression approach to the inference of regulatory networks from gene expression data. Bioinformatics, 21, 3131–3137.PubMedCrossRefGoogle Scholar
  20. 20.
    Tipping, M. and Faul, A. (2003) Fast marginal likelihood maximisation for sparse Bayesian models. In M., B. C. and J., F. B. (eds.), Proceedings of the International Workshop on Artificial Intelligence and Statistics, volume 9.Google Scholar
  21. 21.
    Friedman, N., Linial, M., Nachman, I. and Pe’er, D. (2000) Using Bayesian networks to analyze expression data. Journal of Computational Biology, 7, 601–620.PubMedCrossRefGoogle Scholar
  22. 22.
    Hartemink, A. J., Gifford, D. K., Jaakkola, T. S. and Young, R. A. (2001) Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pacific Symposium on Biocomputing, 6, 422–433.Google Scholar
  23. 23.
    Husmeier, D., Dybowski, R. and Roberts, S. (2005) Probabilistic Modeling in Bioinformatics and Medical Informatics. Advanced Information and Knowledge Processing. Springer, New York.CrossRefGoogle Scholar
  24. 24.
    Heckerman, D. (1999) A tutorial on learning with Bayesian networks. In Jordan, M. I. (ed.), Learning in Graphical Models, Adaptive Computation and Machine Learning, pp. 301–354. MIT Press, Cambridge, Massachusetts.Google Scholar
  25. 25.
    Grzegorczyk, M., Husmeier, D. and Werhli, A. (2008) Reverse engineering gene regulatory networks with various machine learning methods. In Emmert-Streib, F. and Dehmer, M. (eds.), Analysis of Microarray Data: A Network-Based Approach, pp. 101–142. Wiley-VCH, Weinheim.CrossRefGoogle Scholar
  26. 26.
    Geiger, D. and Heckerman, D. (1994) Learning Gaussian networks. In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, pp. 235–243. Morgan Kaufmann, San Francisco, CA.Google Scholar
  27. 27.
    Madigan, D. and York, J. (1995) Bayesian graphical models for discrete data. International Statistical Review, 63, 215–232.CrossRefGoogle Scholar
  28. 28.
    Friedman, N. and Koller, D. (2003) Being Bayesian about network structure. Machine Learning, 50, 95–126.CrossRefGoogle Scholar
  29. 29.
    Grzegorczyk, M. and Husmeier, D. (2008) Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move. Machine Learning, 71, 265–305.CrossRefGoogle Scholar
  30. 30.
    Markowetz, F., Bloch, J. and Spang, R. (2005) Non-transcriptional pathway features reconstructed from secondary effects of RNA interference. Bioinformatics, 21, 4026–4032.PubMedCrossRefGoogle Scholar
  31. 31.
    Fröhlich, H., Fellmann, M., Sultmann, H., Poustka, A. and Beissbarth, T. (2008) Estimating large scale signaling networks through nested effect models with intervention effects from microarray data. Bioinformatics, 24, 2650–2656.PubMedCrossRefGoogle Scholar
  32. 32.
    Markowetz, F., Kostka, D., Troyanskaya, O. and Spang, R. (2007) Nested effects models for highdimensional phenotyping screens. Bioinformatics, 23, i305–i312.PubMedCrossRefGoogle Scholar
  33. 33.
    Fröhlich, H., Tresch, A. and Beissbarth, T. (2009) Nested effects models for learning signaling networks from perturbation data. Biometrical Journal, 51, 304–323.PubMedCrossRefGoogle Scholar
  34. 34.
    Margaritis, D. (2003) Learning Bayesian network model structure from data. Ph.D. thesis, School of Computer Science, Carnegie-Mellon University.Google Scholar
  35. 35.
    Bishop, C. M. (1995) Neural Networks for Pattern Recognition. Oxford University Press, New York, ISBN 0-19-853864-2.Google Scholar
  36. 36.
    Guelzim, N., Bottani, S., Bourgine, P. and Kepes, F. (2002) Topological and causal structure of the yeast transcriptional regulatory network. Nature Genetics, 31, 60–63.PubMedCrossRefGoogle Scholar
  37. 37.
    Battiti, R. and Colla, A. M. (1994) Democracy in neural nets: voting schemes for classification. Neural Networks, 7, 691–707.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Kuang Lin
    • 1
  • Dirk Husmeier
    • 1
  • Frank Dondelinger
    • 2
  • Claus D. Mayer
    • 1
  • Hui Liu
    • 3
  • Leighton Prichard
    • 3
  • George P. C. Salmond
    • 4
  • Ian K. Toth
    • 3
  • Paul R. J. Birch
    • 3
  1. 1.Biomathematics and Statistics ScotlandEdinburgh & AberdeenUK
  2. 2.Biomathematics & Statistics Scotland and School of InformaticsUniversity of EdinburghEdinburghUK
  3. 3.Scottish Crop Research InstituteDundeeUK
  4. 4.Department of BiochemistryUniversity of CambridgeCambridgeUK

Personalised recommendations